The complexity and variability of modern aviation fuels necessitate the development of robust and efficient tools to assess their properties accurately, particularly within the certification framework established by the American Society for Testing and Materials (ASTM). Therefore, previous research has developed predictive models to reduce the experimental burden by predicting aviation fuel properties from broad chemical classes. While two-dimensional Gas Chromatography (GC×GC) provides detailed compositional information, it only identifies the weight of hydrocarbon families (aromatics, cycloalkanes, n-alkanes, iso-alkanes), not individual molecules. Aviation fuels are complex, and their composition can contain more than 60 key classes, the majority of which are isomeric. As a result, an exceptionally high number of possible molecule combinations makes random selection prone to high errors in property prediction. To this end, we used a Monte Carlo approach to search for the optimal combination of 64 hydrocarbon molecules from this vast combinatorial space. By exploring up to 500 million combinations, we aim to determine the molecule set that best predicts mass density, kinematic viscosity, and distillation temperature using linear mixing rules. These rules calculate the properties of molecule mixtures using the weight of each molecule in the mixture and the pure molecules’ properties. We used experimental data for various aviation fuels, including conventional jet fuels, sustainable aviation fuels, and rocket propulsion fuels. Results showed that the isomeric effect has a substantial role in predicting mass density, kinematic viscosity, and the distillation temperature. Results showed that the linear mixing rules could outperform machine learning that overlooks the isomeric effect for the three properties. This research benefits the surrogate fuel analysis, which requires defining a surrogate mixture of hydrocarbon molecules, and will provide insights into the best isomers or molecules to choose to predict aviation fuel properties with the least error. This work will help deliver aviation fuel producers with a relatively accurate pre-screening tool for property prediction, minimizing the need for iterative experimental processes.