CRISPR/Cas-based biocomputation has enabled the design of advanced logic circuits analogous to CPUs. However, the scalability of these systems is limited by the lack of formal verification frameworks. Synthetic genetic circuits (SGCs) fail unpredictably due to resource competition (metabolic load), off-target effects, and crosstalk. The design-build-test cycle is slow and incapable of guaranteeing robustness. This article explores the use of Evolutionary (NEP) and Genetic (NGP) Processor Networks as a unified Bio-CAD (Biological Computer-Aided Design) framework. A conceptual mapping is presented where NGP processors model logic gates, strings model gRNAs and mRNA, and formal filters (rc_s, rc_w) model binding specificity. We argue that the inherently parallel and distributed architecture of NGP, and its demonstrated ability to solve NP-complete problems in linear time, directly reflects the massive parallelism of molecular computation in vivo. This approach would allow for the formal design and verification of fidelity, robustness, and scalability of biocomputers before their costly wetware synthesis.