Inverse Synthetic Aperture Radar (ISAR) is a crucial radar imaging technique that relies on the relative motion between the radar and the target to produce high-resolution images. However, traditional ISAR methods are highly sensitive to inaccuracies in estimating rotational parameters, such as roll, pitch, and yaw. Errors in these estimates can result in significant image degradation. In this paper, we present a novel approach for imaging dynamically rotating scenes using a multistatic ISAR setup, where we estimate motion parameters through a Differential Semblance Optimisation (DSO) criterion. This is done by minimising discrepancies between images formed from multiple transmitter-receiver pairs, our method delivers focused images with improved estimates of the yaw rotation parameter of the targets. We demonstrate the effectiveness of this approach through a series of numerical experiments, highlighting its robustness in both noise-free and noisy environments, and discuss its potential for enhancing ISAR imaging in complex scenarios.