We investigate the beampattern design problem for mono-static multi-user (MU) multi-point-target integrated sensing and communication (ISAC) systems, where a dual-function multiple-input multiple-output (DF-MIMO) base station (BS) performs downlink communication and radar sensing simultaneously. In ISAC systems, sensing and communication inherently compete for resources. As communication demand increases, the beam pattern is reshaped, which might degrade the direction of arrival (DoA) sensing accuracy, measured in terms of mean-squared error (MSE) and lower-bounded by the Cramer-Rao lower bound (CRLB). Since conventional joint formulations of the sensing-based problem often overlook this trade-off, our work addresses it by decomposing the sensing-based problem into two subproblems (SPs). This decomposition enables a more effective exploitation of the beam pattern's physical properties, which we refer to as the Sensing-Guided Communication Dual-Function (SGCDF) beam pattern design. We further develop a low-complexity extension using the Riemannian Manifold Optimization (RMO) and convex closed-set projection. Simulation results confirm that the proposed method improves multi-target estimation accuracy, compared to traditional joint optimization strategies, by preserving the beam pattern, while the low-complexity version offers an excellent performance-complexity tradeoff, maintaining high accuracy with significantly reduced computational cost.