The increasing adoption of structural adhesives in electric vehicle battery packs introduces challenges in predicting joint performance under complex loading and operating conditions. While advanced material models can capture detailed failure mechanisms, their application in early-stage design is often limited by extensive material characterization requirements and high computational cost and time. This study presents a computationally efficient finite element methodology for screening-level assessment of adhesive joint stress distribution and utilization using readily available material data from standard datasheets. The approach is specifically developed for early design stages, where rapid assessment, limited input data, and multiple design iterations are critical. The method is compared against adhesive manufacturer material datasheet strengths using strength-normalized loading to assess consistency of load transfer representation, hotspot locations, and relative stress-utilization trends. It is further extended to evaluate multiple joint configurations, including lap joints, T-joints, and hybrid joints, enabling a broader assessment of adhesive behavior across different loading conditions. A systematic design sensitivity analysis is performed to investigate the influence of key parameters such as adhesive bonding area, and geometric variations on joint stress utilization and hotspot sensitivity. The results highlight the capability of the approach to identify key design sensitivity parameters within the studied joint configurations and support informed decision-making during the concept development phase. The study demonstrates that the proposed methodology enables efficient screening of design concepts, can reduce reliance on high-fidelity material models during early-stage concept screening, and supports reduced evaluation turnaround time. This work establishes a practical right-level modeling strategy that complements advanced simulation techniques by enabling faster iterations, reduced computational effort, and compatibility with existing automotive CAE workflows.