CMOS Image Sensors (CIS) are integral to both human and computer vision tasks, necessitating continuous improvements in key performance metrics such as latency, power, and noise. Despite experienced designers being able to make informed design decisions, novice designers and system architects face challenges due to the complex and expansive design space of CIS. This paper introduces a systematic methodology that elucidates the trade-offs among CIS performance metrics and enables efficient design space exploration. Specifically, we propose a first-principle-based CIS modeling method. By exposing low-level circuit parameters, our modeling method explicitly reveals the impacts of design changes on high-level metrics. Based on the modeling method, we propose a design space exploration process that swiftly evaluates and identifies the optimal CIS design, capable of exploring over 109 designs in under a minute without the need for time-consuming SPICE simulations. Our approach is validated through a case study and comparisons with real-world designs, demonstrating its practical utility in guiding early-stage CIS design.