Mobile robots often face challenges operating in partially known environments where success depends on factors such as robot capabilities, task characteristics, and the environment. This letter presents a generalized, modular framework that enables robots to explicitly account for these factors through the concept of a mission-gain: a pose dependent metric quantifying mission performance (e.g. visibility). When the environment can be proactively explored, a cost map encoding the value of the mission-gain is generated and integrated into a cost function for a model predictive path integral (MPPI) controller, which supports arbitrary mission-specific cost functions. The controller generates inputs that balance progress toward the goal with improved mission performance. The map generation is decoupled from the control execution, making the framework modular and well-suited for heterogeneous multi-robot systems. To address transmission demands of large maps, we distill the map into a Gaussian mixture model (GMM) approximation, yielding a representation with far fewer parameters. The approach is validated in simulations and lab experiments across two scenarios: occlusion-aware navigation and vibration minimization over rough-terrain. The results for both scenarios show the mission-gain aware robot successfully reaching the goal while improving mission safety.