A key challenge in embodied, inter(active) vision is reasoning over alternative hypotheses about the dynamics of perceived objects and events, be it for real-time or even offline interpretation. Towards this, we address the problem of generating and ranking grounded visuospatial hypotheses based on a semantically encoded notion of hypothesis preference. Driven by probabilistic Answer Set Programming (ASP), we propose a general framework for modeling and reasoning about diverse preference types tailored to visuospatial interpretation tasks. The effectiveness of our probabilistic visuospatial hypotheses ranking method is demonstrated and evaluated with a community benchmark of Multi-Object Tracking (MOT17), where modeling uncertainty and preference is critical for robust scene interpretation. Furthermore, practical examples also showcase how semantically driven reasoning with preferences can be effectively used in real-world visual sensemaking tasks.