Knowledge compilation (KC) involves compiling propositional constraints into tractable target languages which in turn efficiently support multiple analyses or queries of the constraints. Solving these queries plays a crucial role in the synthesis and verification of hardware and software systems. Recently, we proposed the target language, Constrained Conjunction & Decision Diagrams (CCDD), experimentally shown to be promising for individual model counting queries. Here, we present the compiler, \(\textsf{Panini}\) , which compiles CNF into CCDD. \(\textsf{Panini}\) supports a range of queries. We present an empirical evaluation focusing on two fundamental queries, uniform sampling and (multiple) model counting, with a wide range of applications. While counting and sampling have witnessed significant performance improvements over the years, scalability still remains the primary challenge. Our evaluation over 600 instances from model counting competitions 2022–2024 show that \(\textsf{Panini}\) achieves state of art compilation by solving 322 instances, which is 183, 148, and 38 more than Dsharp, miniC2D, and D4 respectively. Secondly, on repetitive tasks, \(\textsf{Panini}\) solves 53 and 50 more instances than ExactMC and SharpSAT-TD for model counting, and 175 and 132 more instances than SPUR and KUS for uniform sampling, respectively.